U.S. patent number 5,613,039 [Application Number 08/177,359] was granted by the patent office on 1997-03-18 for apparatus and method for motion detection and tracking of objects in a region for collision avoidance utilizing a real-time adaptive probabilistic neural network.
This patent grant is currently assigned to AIL Systems, Inc.. Invention is credited to James P. Thompson, C. David Wang.
United States Patent |
5,613,039 |
Wang , et al. |
* March 18, 1997 |
Apparatus and method for motion detection and tracking of objects
in a region for collision avoidance utilizing a real-time adaptive
probabilistic neural network
Abstract
Apparatus for motion detection and tracking of objects in a
region for collision avoidance includes a signal transmitter which
provides first and second detection signals for at least partial
reflection by an object located in a spatial region. The apparatus
further includes a signal receiver for receiving the deflected
first and second detection signals corresponding to first and
second object parameter data signals. The apparatus further
includes a Fourier transform circuit for receiving the first and
second object parameter data signals and providing first and second
Fourier transform object parameter data signals. The apparatus
further includes a probabilistic neural network for receiving and
sorting the first and second Fourier transform object parameter
data signals without the use of a priori training data.
Inventors: |
Wang; C. David (Melville,
NY), Thompson; James P. (Greenlawn, NY) |
Assignee: |
AIL Systems, Inc. (Deer Park,
NY)
|
[*] Notice: |
The portion of the term of this patent
subsequent to January 4, 2011 has been disclaimed. |
Family
ID: |
22648296 |
Appl.
No.: |
08/177,359 |
Filed: |
January 3, 1994 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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648194 |
Jan 31, 1991 |
5276772 |
|
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|
Current U.S.
Class: |
706/24; 342/70;
706/10; 706/31; 706/28 |
Current CPC
Class: |
G01J
3/453 (20130101); G06K 9/6273 (20130101); G01S
7/417 (20130101); G01S 7/021 (20130101); G01S
13/931 (20130101); G06N 3/0472 (20130101); G01S
17/95 (20130101); G06N 3/049 (20130101); G01S
2013/93274 (20200101); G01S 2013/93271 (20200101); G01S
13/726 (20130101); G01S 13/24 (20130101); Y02A
90/10 (20180101); G01S 2013/93272 (20200101); G01S
7/2883 (20210501); G01S 13/348 (20130101) |
Current International
Class: |
G01S
17/95 (20060101); G01J 3/45 (20060101); G01S
7/02 (20060101); G01S 13/00 (20060101); G01S
13/93 (20060101); G01J 3/453 (20060101); G01S
17/00 (20060101); G01S 7/41 (20060101); G06N
3/04 (20060101); G06N 3/00 (20060101); G01S
13/34 (20060101); G01S 13/72 (20060101); G01S
13/24 (20060101); G01S 7/288 (20060101); G01S
7/285 (20060101); G06F 015/18 (); G01S
013/00 () |
Field of
Search: |
;342/28,70,90,95,118,129,195,196,71 ;395/22,21,27,11,24
;364/516,517 ;340/436 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
"An Optical Fourier/Electronic Neurocomputer Automated Inspection
System", by D.E. Glover, IEEE International Conference on Neural
Networks, pp. I-569 to I-576 (Jul. 1988). .
"A VLSI Implementation of a Neural Car Collision Avoidance
Controller", by Nijhuis, et al., IEEE International Joint
Conference on Neural Networks, pp. I-493 to I-499 (Jul. 1991).
.
"An Adaptive Data Sorter Based on Probabilistic Neural Networks",
by Wang, et al., IEEE International Conference on Neural Networks,
pp. 1296 through 1302 (Nov. 1991). .
"Minimum Description Length Pruning and Maximum Mutual Information
Training of Adaptive Probabilistic Neural Networks", by Fakhr, et
al., IEEE International Conference on Neural Networks, pp. 1338
through 1342 (Apr. 1993). .
"Probabalistic Neural Networks for Classification, Mapping, or
Associative Memory", by Donald F. Specht, IEEE International
Conference on Neural Networks, vol. 1, pp. 525-532 (Jul. 1988).
.
"The Use of Probabilistic Neural Networks to Improve Solution Times
for Hull-to-Emitter Correlation Problems", by P. Susie Maloney and
Donald F. Specht, published by the International Joint Conference
on Neural Networks, vol. 1, pp. 289-294 (Jun. 1989). .
"An Application of Neural Net Technology to Surveillance
Information Correlation and Battle Outcome Prediction" by P. Susie
Maloney, published by the IEEE pp. 948-955 (1989). .
"Probabalistic Neural Networks and the Polynomial Adaline as
Complementary Techniques for Classification" by Donald F. Specht,
Published in IEEE Transactions of Neural Networks, vol. 1, No. 1,
pp. 111-121 (Mar. .
"Survey of Neural Network Technology for Automatic Target
Recognition" By Michael W. Roth, published in IEEE Transactions of
Neural Networks, vol. 1, No. 1, pp. 28-43 (Mar. 1990). .
Wang et al., "An Adaptive Data Sorter Based on Probabilistic Neural
Networks," 1991 Conf.: National Aerospace and Electronics, May
1991, 1096-1102. .
Xu et al., "Optimum Frequencies Selection for Radar Target
Classification by Neural Network," IEEE Int'l. Conf. Neural
Networks, Nov. 1991, 1236-1241. .
Steen et al., "The Application of Feed-Forward Connectionist Models
to ESM Bearing Estimation Using Signal Amplitude," IEE Colloq.
Signal Processing Techniques for Electronic Warfare, Jan. 1992,
4/1-4/6. .
Kim et al., "Generalized Probabilistic Neural Network Based
Classifiers," IEEE Int'l. Conf. Neural Networks, Jun. 1992,
III-648-653. .
Kim et al, "Neural Network Based Optimum Radar Target Detection in
Non-Gaussian Noise," IEEE Int'l. Conf. Neural Networks, Jun. 1992,
III-654-659..
|
Primary Examiner: Downs; Robert W.
Attorney, Agent or Firm: Hoffmann & Baron
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATION
This application is a continuation-in-part of U.S. patent
application Ser. No. 07/648,194 filed Jan. 31, 1991, now U.S. Pat.
No. 5,276,772, the disclosure of which is incorporated herein by
reference.
Claims
We claim:
1. Apparatus for detecting and tracking motion of objects in a
region for collision avoidance comprising:
a signal transmitter having an input port and an output port, the
signal transmitter being responsive to first and second detection
signals at the input port and transmitting the first and second
detection signals from the output port to a spatial region, the
first and second detection signals at least partially reflecting
off at least one object located in the spatial region, the first
detection signal having a first signal frequency and the second
detection signal having a second signal frequency, the first signal
frequency being substantially distinct from the second signal
frequency,
a signal receiver being electrically coupled to the transmitter,
the receiver having an input port and an output port, the signal
receiver being responsive to the at least partially reflected first
and second detection signals respectively corresponding to first
and second object parameter data signals,
a probabilistic neural network processor being electrically coupled
to the signal receiver and being responsive to the first and second
object parameter data signals, the probabilistic neural network
providing an output signal indicative of the proximity of an object
detected in the spatial region; said probabilistic neural network
including:
(a) a plurality of cluster processor circuits being responsive to
the first and second object parameter data signals, each cluster
processor circuit generating an output signal representing a
probability density function estimation value corresponding to the
received first and second object parameter data signals, each
cluster processor circuit including:
(1) an input buffer memory circuit, the input buffer memory circuit
having a plurality of serially connected registers for storing the
first and second object parameter data signals assigned to a
respective cluster processor circuit;
(2) a plurality of processing elements, each of the processing
elements being coupled to a corresponding register of the input
buffer memory circuit and being responsive to assigned first and
second object parameter data signals stored in the input buffer
memory circuit, each of the processing elements further being
responsive to current unassigned first and second object parameter
data signals, each processing element providing an output
signal;
(3) a plurality of exponential function circuits, each of the
exponential function circuits being coupled to a corresponding
processing element, each exponential function circuit performing an
exponential function on the output signal of each processing
element and providing an output signal in response thereto; and
(4) a summation circuit coupled to each of the exponential function
circuits of the respective cluster processor circuit, the summation
circuit being responsive to the output signals from the exponential
function circuits, performing an addition function thereon and
providing an output signal representing a probability density
function estimation value for each unassigned first and second
object parameter data signal;
(b) a decision logic circuit, the decision logic circuit being
coupled to the summation circuit of each cluster processor circuit,
the decision logic circuit comparing the output signal of each
summation circuit of the corresponding cluster processor circuit
with at least first and second threshold value signals, and
providing a decision address signal in response thereto;
(c) a switching circuit, the switching circuit being coupled to the
decision logic circuit and to each of the cluster processor
circuits and further being responsive to current unassigned first
and second object parameter data signals and assigning the current
unassigned first and second object parameter data signals to a
respective cluster processor circuit for storage in the input
buffer memory circuit of the respective cluster processor circuit
in response to the decision address signal from the decision logic
circuit, the switching circuit being electrically coupled to the
input buffer memory circuit of each cluster processor circuit, the
switching circuit assigning the current unassigned first and second
object parameter data signal to the input buffer memory circuit of
a currently operating cluster processor circuit if the summation
circuit output signal representing a probability density function
estimation value of the currently operating cluster processor
circuit is at least equal to the first threshold value signal, the
switching circuit assigning the current unassigned first and second
object parameter data signal to the input buffer memory circuit of
a newly operating cluster processor which was previously a
non-operating cluster processor if the summation circuit output
signal representing the probability density function estimation
value of each currently operating cluster processor circuit is less
than or equal to the second threshold value signal; and
(d) a storage register circuit, the storage register circuit being
electrically coupled to the switching circuit, the switching
circuit providing the current unassigned first and second object
parameter data signals to the storage register circuit when the
output signal of the summation circuit of each cluster processor
circuit is less than the first threshold value signal and greater
than the second threshold value signal, the unassigned first and
second object parameter data signals being provided to the storage
register circuit for later detailed analysis by the probabilistic
neural network.
2. Apparatus as defined by claim 1, which further comprises:
a lowpass filter electrically coupled to the signal receiver, the
lowpass filter substantially permitting passage of at least a
portion of the first and second object parameter data signals less
than a first frequency, the lowpass filter being responsive to the
first and second object parameter data signals from the receiver
and providing a lowpass filter output signal, the lowpass filter
output signal being provided to the probabilistic neural network
processor.
3. Apparatus as defined by claim 1, which further comprises:
an analog to digital converter, the analog to digital converter
being electrically coupled to the signal receiver, the analog to
digital converter being responsive to the first and second object
parameter data signals and providing a plurality of digital first
and second object parameter data signals, the plurality of digital
first and second object parameter data signals being provided to
the probabilistic neural network processor.
4. Apparatus as defined in claim 1, which further comprises:
a Fourier transform circuit being electrically coupled to the
signal receiver, the Fourier transform circuit being responsive to
the first and second object parameter data signals, the Fourier
transform circuit providing a first and second Fourier transform
object parameter data signals representing a spectral waveshape of
the first object parameter data signal and the second object
parameter data signal.
5. Apparatus as defined by claim 4, which further comprises:
a peak detector electrically coupled to the Fourier transform
circuit, the peak detector receiving the first Fourier transform
object parameter data signal, the peak detector providing a peak
detector output signal indicative of the amplitude of the first
object parameter data signal at a peak amplitude of the Fourier
transform output parameter data signal, the peak detector output
signal being provided to the probabilistic neural network
processor, the peak detector output signal being indicative of a
velocity of the object detected in the spatial region.
6. Apparatus as defined by claim 5, which further comprises:
a subtractor coupled to the Fourier transform circuit and the peak
detector, the subtractor being responsive to the peak detector
output signal and the second Fourier transform object parameter
detector signal, the subtractor providing a subtractor output
signal indicative of the difference between the peak detector
output signal and the second Fourier transform output parameter
data signal, the subtractor output signal representing a range of
the object detected in the spatial region.
7. Apparatus as defined in claim 1, which further comprises:
a signal generator electrically coupled to the input port of the
signal transmitter, the signal generator providing at least the
first detection signal and the second detection signal to the
signal transmitter for transmitting to the spatial region.
8. Apparatus as defined in claim 1, which further comprises:
an antenna electrically coupled to the signal transmitter output
port and the signal receiver input port, the antenna being
responsive to the first and second detection signals from the
signal transmitter, the antenna transmitting the first and second
detection signals to the spatial region, the antenna being
responsive to the at least partially reflected first and second
detection signals.
9. Apparatus as defined in claim 1, which further comprises:
switching means electrically coupled to the signal receiver output
port for switching the first and second object parameter data
signals, the switching means having an input port and at least
first and second output ports, the switching means input port
receiving both the first and second object parameter data signals,
the switching means first output port providing the first object
parameter data signal and the switching means second output port
providing the second object parameter data signal.
10. Apparatus for detecting and tracking motion as defined by claim
1, wherein the first and second detection signals are continuous
wave signals.
11. An apparatus as defined in claim 1, wherein said first
threshold value signal is about 70% and said second threshold value
signal is about 10%.
12. Apparatus for detecting and tracking motion of objects in a
region for collision avoidance comprising:
a signal transmitter having an input port and an output port, the
signal transmitter being responsive to first and second detection
signals at the input port and transmitting the first and second
detection signals from the output port to a spatial region, the
first and second detection signals at least partially reflecting
off at least one object located in the spatial region, the first
detection signal having a first signal frequency and the second
detection signal having a second signal frequency, the first signal
frequency being substantially distinct from the second signal
frequency,
a signal receiver being electrically coupled to the transmitter,
the receiver having an input port and an output port, the signal
receiver being responsive to the at least partially reflected first
and second detection signals respectively corresponding to first
and second object parameter data signals,
a Fourier transform circuit being electrically coupled to the
signal receiver and being responsive to the first and second object
parameter data signals, the Fourier transform circuit providing
first and second Fourier transform object parameter data signals
representing a spectral waveshape of the first object parameter
data signal and the second object parameter data signal, and
a probabilistic neural network processor being electrically coupled
to the Fourier transform circuit and being responsive to the first
and second object parameter data signals, the probabilistic neural
network providing an output signal indicative of the proximity of
an object detected in the spatial region, the probabilistic neural
network comprising:
(a) a plurality of cluster processor circuits being responsive to
the first and second Fourier transform object parameter data
signals, each cluster processor circuit generating an output signal
representing a probability density function estimation value
corresponding to the received first and second Fourier transform
object parameter data signals, each cluster processor circuit
including:
(1) an input buffer memory circuit, the input buffer memory circuit
having a plurality of serially connected registers for storing the
first and second Fourier transform object parameter data signals
assigned to a respective cluster processor circuit;
(2) a plurality of processing elements, each of the processing
elements being coupled to a corresponding register of the input
buffer memory circuit and being responsive to assigned first and
second Fourier transform object parameter data signals stored in
the input buffer memory circuit, each of the processing elements
further receiving current unassigned first and second Fourier
transform object parameter data signals, each processing element
providing an output signal;
(3) a plurality of exponential function circuits, each of the
exponential function circuits being coupled to a corresponding
processing element, each exponential function circuit performing an
exponential function on the output signal of each processing
element and providing an output signal in response thereto; and
(4) a summation circuit coupled to each of the exponential function
circuits of the respective cluster processor circuit, the summation
circuit being responsive to the output signals from the exponential
function circuits, performing an addition function thereon and
providing an output signal representing a probability density
function estimation value for each unassigned first and second
Fourier transform object parameter data signal;
(b) a decision logic circuit, the decision logic circuit being
coupled to the summation circuit of each cluster processor circuit,
the decision logic circuit comparing the output signal of each
summation circuit of the corresponding cluster processor circuit
with at least first and second threshold value signals, and
providing a decision address signal in response thereto;
(c) a switching circuit, the switching circuit being coupled to the
decision logic circuit and to each of the cluster processor
circuits and further being responsive to current unassigned first
and second Fourier transform object parameter data signals and
assigning the current unassigned first and second Fourier transform
object parameter data signals to a respective cluster processor
circuit for storage in the input buffer memory circuit of the
respective cluster processor circuit in response to the decision
address signal from the decision logic circuit, the switching
circuit being electrically coupled to the input buffer memory
circuit of each cluster processor circuit, the switching circuit
assigning the current unassigned first and second Fourier transform
object parameter data signals to the input buffer memory circuit of
a currently operating cluster processor circuit if the summation
circuit output signal representing a probability density function
estimation value of the currently operating cluster processor
circuit is at least equal to the first threshold value signal, the
switching circuit assigning the current unassigned first and second
Fourier transform object parameter data signals to the input buffer
memory circuit of a newly operating cluster processor which was
previously a non-operating cluster processor if the summation
circuit output signal representing the probability density function
estimation value of each currently operating cluster processor
circuit is less than or equal to the second threshold value signal;
anal
(d) a storage register circuit, the storage register circuit being
electrically coupled to the switching circuit, the switching
circuit providing the current unassigned first and second Fourier
transform object parameter data signals to the storage register
circuit when the output signal of the summation circuit of each
cluster processor circuit is less than the first threshold value
signal and greater than the second threshold value signal, the
unassigned first and second Fourier transform object parameter data
signals being provided to the storage register circuit for later
detailed analysis by the probabilistic neural network.
13. Apparatus for detecting and tracking motion as defined by claim
12, wherein the first and second detection signals are continuous
wave signals.
14. An apparatus as defined in claim 12, wherein said first
threshold value signal is about 70% and said second threshold value
signal is about 10%.
15. A method of detecting and tracking motion of objects in a
region for collision avoidance which comprises:
a) transmitting first and second detection signals having distinct
frequencies for at least partial reflection by at least one object
in a spatial region, the first detection signal having a first
signal frequency and the second detection signal having a second
signal frequency, the first signal frequency being substantially
distinct from the second signal frequency;
b) detecting the first and second detection signals at least
partially reflected by the object, the first and second detection
signals corresponding to first and second object parameter data
signals;
c) providing the first and second object parameter data signals to
a probabilistic neural network for sorting the first and second
object parameter data signals without the use of a priori training
data, step (c) including the additional sub-steps of:
i) inputting current unassigned first and second object parameter
data signals into at least one of a plurality of processing
elements contained within a plurality of currently operating
cluster processor circuits;
ii) generating a first signal representing a probability density
function estimation value in response to the first and second
object parameter data signal of each currently operating cluster
processor circuit of the plurality of cluster processors, the
probability density function estimation value signal being
generated using the current unassigned first and second object
parameter data signals and using a plurality of assigned first and
second object parameter data signals stored in an input buffer
memory circuit of each currently operating cluster processor
circuit;
iii) comparing the first signal representing the probability
density function estimation value generated by each currently
operating cluster processor circuit to at least first and second
threshold value signals in a decision logic circuit;
iv) generating a decision address signal in response to the
comparison of the first signal and the first and second threshold
value signals, the decision address signal being provided to a
switching circuit, the decision address signal denoting a currently
operating cluster processor circuit when the first signal
representing the probability density function estimation value is
at least equal to the first threshold value signal, the decision
address signal denoting and activating a non-operating cluster
processor circuit when the first signal representing the
probability density function estimation value is at most equal to
the second threshold value signal;
v) assigning the current unassigned first and second object
parameter data signal from the switching circuit to the cluster
processor circuit corresponding to the decision address signal
provided to the switching circuit;
vi) storing the current unassigned first and second object
parameter data signal in the input buffer memory circuit of the
cluster processor circuit according to the decision address signal
received by the switching circuit; and
vii) denoting a storage register circuit and assigning the current
unassigned first and second object parameter data signal from the
switching circuit to the storage register circuit for temporary
storage therein when the first signal, representing the probability
density function estimation value, is less than the first threshold
value signal and greater than the second threshold value signal,
the unassigned first and second object parameter data signal being
provided to the storage register circuit for later detailed
analysis by the probabilistic neural network.
16. A method as defined by claim 15, which includes the further
step of manually setting the level of at least one of the first and
second threshold value signals.
17. A method as defined by claim 15, wherein the first threshold
value signal is greater in magnitude than the second threshold
value signal.
18. A method as defined by claim 15, wherein prior to step (c), the
method further includes Fourier transforming the first and second
object parameter data signals to provide Fourier transformed first
and second object parameter data signals.
19. A method of detecting and tracking motion as defined by claim
15, wherein the first and second detection signals are continuous
wave signals.
20. A method as defined in claim 15, wherein in step (c)(iii), said
first threshold value signal is about 70% and said second threshold
value signal is about 10%.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to probabilistic neural networks, and more
particularly relates to an adaptive probabilistic neural network
that can sort input parameter data signal description words,
relating to the relative motion of objects, without the use of a
priori training data.
2. Description of the Prior Art
Radar emitter pulse sorting and radar emitter identification are
the primary functions of electronic support measure (ESM) and
electronic counter measure (ECM) systems. There are three basic
steps involved in the emitter identification process. First, the
input pulse signals undergo an initial level of analysis and
differentiation commonly referred to as "sorting" or
"pulse-by-pulse deinterleaving". The sorting process involves
analysis of the input signals to achieve an initial grouping of
pulses from each emitter in the collected pulse sequence. If a high
percentage of pulses are correctly sorted and grouped during the
first sorting level, then only a small number of pulses will
undergo a second level of deinterleaving. However, invariably many
input pulse signals are not capable of being correctly sorted
because the signals can not be easily differentiated by the system.
The sorting system is not able to recognize the input signals
because the input signals are often noisy, inaccurate and corrupt
with additional or missing signal parameter components or
information. The pulse groups which were not correctly sorted and
grouped by the system at the first level of analysis require a
second level of analysis commonly referred to as "second level
deinterleaving". This second processing and sorting level requires
multiple and complex sorting algorithms which occupy a great deal
of computer time. Once all of the input signals have been sorted
and deinterleaved by the first and second levels of analysis, they
are transferred to a third stage of processing commonly referred to
as emitter identification. During this stage, the sorted groups are
analyzed so that the radar emitter transmitting each type of signal
can be identified for ESM and ECM purposes.
In the past, various rule-based techniques were developed for
sorting digitized pulse signals. One of the earlier rule-based
sorting systems is commonly referred to as the histogram method.
The histogram method compares each input pulse parameter signal
against a group of preset signal parameters. The comparison is
performed to determine if the parameters of the input pulse
parameter signal can be classified within the group of preset
signal parameter values. However, the histogram method may not
accurately sort the incoming signal when even one parameter of the
input pulse parameter signal does not match the preset signal
parameter values. This makes the histogram method undesirable. The
histogram method is also undesirable because the incoming signals
must be input to the system at a relatively slow rate as compared
to the rate that the pulse signals are transmitted by the radar
emitter. Therefore, a sorting system utilizing the histogram method
is not readily able to produce a real time system response to
incoming radar pulse signals.
Another early rule-based sorting technique is commonly referred to
as "adaptive binning." Adaptive binning compares individual
parameters of the input pulse signal to preset signal parameter
values. Each input pulse signal can have numerous parameter values.
The adaptive binning system is relatively slow in operating because
only one parameter comparison is undertaken at a time. Therefore,
successive comparisons are not made until preceding comparisons are
complete.
Additionally, the adaptive binning system is very rigid, inflexible
and incapable of sorting input signals having parameter value
errors. For example, if an input pulse signal consists of ten
parameters, and one parameter of the group of ten parameters is out
of range because the signal is noisy and incapable of being
properly read by the system, the input pulse signal would not be
correctly sorted. This type of incorrect sorting can occur even if
the remaining nine signal parameters match the corresponding preset
signal parameter values exactly. Since the system is so inflexible
and incapable of sorting inputs having only one corrupt input pulse
parameter, optimal results for sorting real data only approach
approximately 88% accuracy. The adaptive binning system is also
undesirable because it can not easily provide a "joint" quality
measurement of system performance and sorting accuracy.
It has been proposed by Donald F. Specht, in his article,
"Probabilistic Neural Networks for Classification, Mapping, or
Associative Memory", published in the Proceedings of the 1988 IEEE
International Conference on Neural Networks, Vol. 1, pp. 525-32,
July 1988, to use a probabilistic neural network (PNN) to recognize
input signals based upon a priori test data. Specht proposed using
a PNN to search incoming data signals for a priori data patterns.
The a priori test data is essentially a library or directory of
patterns representing a database for the system. The probabilistic
neural network developed by Specht is a multi-layer feed-forward
network which uses sums of Gaussian distributions to estimate a
probability density function based upon a group of a priori
training patterns. The estimated probability density function is
then used to sort and match new input data to the a priori training
patterns.
In another article, "The Use of Probabilistic Neural Networks to
Improve Solution Times for Hull-To-Emitter Correlation Problems",
published by the International Joint Conference on Neural Networks,
Vol. 1, pp. 289-94, June 1989, P. Susie Maloney and Donald F.
Specht disclose applying a probabilistic neural network to
hull-to-emitter correlation problems for electronic intelligence
(ELINT) systems. However, this process operates utilizing already
sorted pulse data and does not use a probabilistic neural network
for real time, non a priori pulse sorting. Real time, non a priori
pulse sorting is difficult because real data input signals are
often noisy, inaccurate, and corrupt with additional or missing
signal parameter components and information. In addition, the
output probability density function for a specific signal emitter
may have multiple disjoint boundaries where an individual boundary
may be overlapped with another emitter probability density
function. Such input signal parameters cannot be accurately
approximated by an n-dimensional Gaussian distribution as proposed
by Specht.
Today, a great many automobiles are sold with elaborate safety
systems that are designed to help the driver and passengers of the
vehicle survive a collision. These safety systems often include
seat-belts, air-bags, anti-lock brakes and side-impact restraint
systems. The current vehicle safety systems (collision survival
devices) are an improvement over automobiles that did not have such
safety systems. In fact, safety systems are quickly becoming an
important selling point for automobile manufacturers and salesman,
and an important consideration for consumers when purchasing an
automobile. As important as these safety systems have become, a
reliable collision warning system which would obviate the need for
collision survival devices is yet to be developed and marketed for
consumer use.
Collision warning radar systems have only recently been tested and
incorporated for use in motor vehicles. Studies have determined the
benefit of including collision warning devices in motor vehicles
stating that sixty percent of roadway collisions could be avoided
if the operator of the vehicle was provided warning at least
one-half second prior to a collision. Current collision warning
systems operate by attempting to measure the relative position,
speed and direction of objects to determine whether the objects
being monitored are moving closer or farther away (i.e., presenting
a greater or lesser threat of collision) from the motor vehicle.
These systems are relatively unreliable and often reach alarm
condition prematurely or at a time when the collision can not be
avoided. In addition, current collision warning systems are
incapable of reliably tracking a plurality of closely located
objects (i.e., cars, highway dividers, trees, barriers, etc.) and
are incapable of reliably determining if an object is presenting a
greater or lesser danger for collision so as to provide adequate
warning of a potential collision.
OBJECTS AND SUMMARY OF THE INVENTION
It is an object of the present invention to provide a system and
method to sort unknown input object data signals of more than one
object without the use of a priori training data.
It is yet a further object of the present invention to provide a
collision avoidance and object detection signal identification
sorting system and method which overcomes the inherent
disadvantages of known collision avoidance and object detection
sorting techniques.
In accordance with one form of the present invention, apparatus for
motion detection and tracking of objects in a region for collision
avoidance includes at least a signal transmitter, a signal receiver
and a probabilistic neural network processor.
The signal transmitter includes an input port for receiving a first
and second detection signal provided by a signal generator, and an
output port for transmitting the first and second detection signals
to a spatial region. Upon interaction with an object in the spatial
region, the first and second detection signals are at least
partially reflected by the object toward the signal receiver.
The signal receiver is electrically coupled to the signal
transmitter and includes an input port and an output port. The
input port of the signal receiver receives the at least partially
reflected first and second detection signals which respectively
correspond to first and second object parameter data signals. The
first and second object parameter data signals are thereafter
provided to the probabilistic neural network processor.
The probabilistic neural network processor is electrically coupled
to the signal receiver so as to receive the first and second object
parameter data signals. The probabilistic neural network provides
an output signal indicative of the likelihood and threat of a
collision with the object. The probabilistic neural network
includes at least one cluster processor circuit, a decision logic
circuit and a switching circuit.
The cluster processor circuit includes an input buffer memory
circuit essentially consisting of a group of serially connected
memory circuits (i.e., registers). Input parameter data signals
which are provided to the neural network by the interferogram
detector/converter are temporarily stored in the serially connected
memory circuits of the input buffer memory circuit. The cluster
processor circuit also includes a group of processing elements
connected to the input buffer memory circuit, exponential function
circuits coupled to corresponding processing elements and a
summation circuit connected to each exponential function circuit.
These components interact to generate a probability density
function estimation value signal for the cluster processor circuit.
The probability density function estimation value signal is
generated by using both assigned input parameter data signals
temporarily stored in the memory circuits and current unassigned
input molecular parameter data signals. The probability density
function estimation value signal of a cluster processor circuit
represents the probability that the current unassigned input
parameter data signal closely matches or belongs to the group of
assigned input parameter data signals currently stored in the
corresponding cluster processor circuit.
The decision logic circuit is coupled to the summation circuit of
each cluster processor circuit. The decision logic circuit compares
the output signal of each summation circuit to at least a first
threshold value signal, and more preferably, also to a second
threshold value signal. The output signal of each summation circuit
corresponds to a probability density function estimation value of
the cluster processor circuit. The decision logic circuit
comparison is made to determine if the probability density function
estimation value signal is at least equal to the first threshold
value signal, at most equal to the second threshold value signal,
or less than the first threshold value signal and greater than the
second threshold value signal. The comparison in the decision logic
circuit occurs simultaneously for all currently operating cluster
processor circuits. The sorting process can be characterized as an
internal competition among the currently operating cluster
processor circuits to determine which currently operating cluster
processor circuit has the greatest probability density function
estimation value signal and which assigned input molecular
parameter data signal stored in the respective cluster processor
circuit best matches the current unassigned input molecular
parameter data signal.
The decision logic circuit is coupled to the switching circuit. The
decision logic circuit generates and provides a decision address
signal to the switching circuit. The decision address signal
corresponds to the cluster processor circuit currently storing
assigned input parameter data signals that best match the current
unassigned input parameter data signal (i.e., the cluster processor
circuit having the highest probability density function estimation
value signal). If the probability density function estimation value
signal of all currently operating cluster processor circuits is
less than the first threshold value signal and less than the second
threshold value signal, then the decision logic circuit provides a
decision address signal to the switching circuit instructing the
switching circuit to activate a previously non-operating cluster
processor circuit.
The switching circuit receives both the decision address signal and
the current unassigned input parameter data signal being
scrutinized. The switching circuit then provides the current
unassigned input parameter data signal to the input buffer memory
circuit of the cluster processor circuit corresponding to the
decision address signal transmitted by the decision logic circuit.
The newly assigned input parameter data signal is temporarily
stored in the first register of the input buffer memory circuit of
the corresponding cluster processor circuit. When the current input
parameter data signal is sorted and stored in the input buffer
memory circuit, the previously stored input molecular parameter
data signals are sequentially transmitted to the next register in
the group of serially connected registers for further storage.
Therefore, upon introduction of the current input parameter data
signal to the first register, the assigned input parameter data
signal that was previously stored in the first register is shifted
to the second register of the input buffer memory circuit.
Furthermore, the input parameter data signal stored in the second
register is shifted to the third register and so on. The input
parameter data signal that was in the last register of the input
buffer memory circuit is considered "outdated" and not valuable for
determining the next probability density function estimation value
signal. Accordingly, this "outdated" input parameter data signal is
discarded and replaced by a subsequent input parameter data
signal.
The switching circuit will activate a previously non-operating
cluster processor circuit when the probability density function
estimation value signal computed for all currently operating
cluster processor circuits is less than the first and the second
threshold value signals. For example, a probability density
function estimation value signal of less than 10% for every
currently operating cluster processor circuit indicates that the
current unassigned input parameter data signal does not match the
assigned input parameter data signals stored in the currently
operating cluster processor circuits. Accordingly, a previously
non-operating cluster processor circuit is activated to store the
current unassigned input molecular parameter data signal. The new
cluster processor circuit corresponds to a new sorting group. The
newly assigned input molecular parameter data signal will be stored
in the first register of the input buffer memory circuit of the
newly activated cluster processor circuit.
A preferred form of the apparatus for motion detection of objects
in a region for collision avoidance utilizing a real time adaptive
probabilistic neural network system and method for data sorting and
storing, as well as other embodiments, objects, features and
advantages of this invention, will be apparent from the following
detailed description of illustrative embodiments thereof, which is
to be read in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of an adaptive probabilistic neural
network system utilized in the present invention.
FIG. 2 is a block diagram of one cluster processor used in the
neural network shown in FIG. 1.
FIG. 3 is a graphical representation of a decision boundary of a
2-dimensional probabilistic neural network, after sorting one input
parameter data signal.
FIG. 4 is a graphical representation of a decision boundary of a
2-dimensional probabilistic neural network, after sorting two input
parameter data signals.
FIG. 5 is a graphical representation of a decision boundary of a
2-dimensional probabilistic neural network, after sorting three
input parameter data signals.
FIG. 6 is a block diagram of an apparatus for motion detection of
objects in a region for collision avoidance in accordance with the
present invention.
FIG. 7 is a block diagram of velocity and range finder circuit of
FIG. 6 coupled to the Fourier transform circuit.
FIG. 8 is a graphical representation of a probability density
function decision boundary after sorting a first input sample.
FIG. 9 is a graphical representation of a probability density
function decision boundary after sorting first and second input
samples.
FIG. 10 is a graphical representation of a probability density
function decision boundary after sorting first, second and third
input samples.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Referring now to FIG. 1 of the drawings, a preferred real time data
sorting adaptive probabilistic neural network (APNN) constructed in
accordance with the present invention will now be described. The
APNN includes a plurality of identical subunits commonly referred
to as cluster processor circuits 10. Each cluster processor circuit
may be classified as a currently operating cluster processor
circuit, a newly operating cluster processor circuit or a
non-operating cluster processor circuit. Each cluster processor
circuit is operatively coupled to a decision logic circuit 11 and
to a switching circuit 13. Each currently operating cluster
processor circuit generates and provides a probability density
function estimation value signal to the decision logic circuit. The
decision logic circuit is also coupled to the switching circuit.
The decision logic circuit generates and provides a decision
address signal to the switching circuit. The decision address
signal identifies the cluster processor circuit which results in
correct sorting and temporary storage of a current unassigned input
pulse parameter data signal 18.
A preferred form of the cluster processor circuit 10 is illustrated
by FIG. 2. The cluster processor circuit includes a plurality of
serially connected registers 14 (i.e., buffer memory circuits). The
serially connected registers store the input pulse parameter data
signal 18 assigned to the respective cluster processor circuit by
the switching circuit 13. The combination of serially connected
registers within the cluster processor circuit defines a pulse
buffer memory circuit 19.
Each cluster processor circuit 10 also includes a plurality of
processing elements 15. Each of the plurality of processing
elements is coupled to a corresponding register 14 of the pulse
buffer memory circuit 19. Each processing element receives assigned
input pulse parameter data signals stored in a respective register
of the pulse buffer memory circuit of the cluster processor
circuit. Each processing element is also coupled to the system
input which allows each processing element to receive the current
unassigned input pulse parameter data signal 18. As described
below, a processing element is designed to generate and transmit a
signal to an exponential function circuit 16.
The cluster processor circuit 10 also includes a plurality of
exponential function circuits 16. Each of the plurality of
exponential function circuits is coupled to a corresponding
processing element 15. Each exponential function circuit is
configured to receive the output signal of only one of the
plurality of processing elements. Each exponential function circuit
performs an exponential function on the output signal of the
processing element.
The cluster processor circuit 10 also includes a summation circuit
17. The summation circuit is coupled to each of the plurality of
exponential function circuits 16 of the respective cluster
processor circuit. The output signal of each exponential function
circuit is received by the summation circuit. The summation circuit
processes the exponential function circuit output signals of the
corresponding cluster processor circuit. The output signal of the
summation circuit also represents the output signal of the
corresponding cluster processor circuit. The output signal of the
cluster processor circuit corresponds to a probability density
function estimation value of the cluster processor circuit. A
probability density function estimation value is simultaneously
calculated for all currently operating cluster processor circuits.
A probability density function estimation value signal represents
the probability that the current unassigned input pulse parameter
data signal matches or belongs to the group of assigned input pulse
parameter data signals currently stored in the registers 14 of the
pulse buffer memory circuit 19 of the respective cluster processor
circuit. The cluster processor circuits 10 of the neural network
system may employ parallel processing using transputers. A suitable
transputer which may be used is Part No. MTM-PC, which is a
reconfigurable multi-transputer, manufactured by Inmos
Corporation.
The decision logic circuit 11 is coupled to the summation circuit
17 of each cluster processor circuit 10. The decision logic circuit
includes a plurality of comparators. The decision logic circuit is
designed to compare the probability density function estimation
value signal of each currently operating cluster processor circuit
to at least a first threshold value signal. The comparison process
occurs simultaneously for all currently operating cluster processor
circuits. In response to this comparison, a decision address signal
is generated and transmitted by the decision logic circuit to the
switching circuit 13. The decision address signal represents the
cluster processor circuit currently storing the assigned input
pulse parameter data signals which best match the current
unassigned input pulse parameter data signal. The decision address
signal can correspond to any currently operating cluster processor
circuit or it can activate a non-operating cluster processor
circuit so that the pulse buffer memory circuit 19 of the newly
operating cluster processor circuit will store the current input
pulse parameter data signal.
The switching circuit 13 is operatively coupled to the decision
logic circuit 11 and to the pulse buffer memory circuit 19 of each
cluster processor circuit. The switching circuit also receives the
current unassigned input pulse parameter data signal 18. Upon
receiving the decision address signal from the decision logic
circuit, the switching circuit transmits the current unassigned
input pulse parameter data signal to the pulse buffer memory
circuit 19 of the cluster processor circuit 10 corresponding to the
decision address signal received.
A storage register circuit 12 is coupled to the switching circuit
13. The storage register circuit receives the current unassigned
input pulse parameter data signal transferred by the switching
circuit. The switching circuit transfers, to the storage register
circuit, the input pulse parameter data signals which can not be
properly sorted by the present sorting method. This assignment to
the storage register circuit corresponds to a probability density
function estimation value signal of the respective cluster
processor circuit which is less than the first threshold value
signal and greater than the second threshold value signal. The
input pulse parameter data signal in the storage register is
maintained for possible future analysis and processing. Therefore,
the input pulse parameter data signal may be correctly sorted to
one of the plurality of cluster processor circuits.
The operation of the real time adaptive probabilistic neural
network (APNN) for data sorting, constructed in accordance with the
present invention, will now be described. Initially, the registers
14 of the pulse buffer memory circuit 19 of each cluster processor
circuit 10 are empty and contain no assigned input pulse parameter
data signals. The APNN system is designed so that a priori training
data does not have to be stored in the registers at the beginning
of system operation in order to effectuate sorting. The APNN system
operates to develop its own sorting groups as the current
unassigned input pulse parameter data signals 18 are introduced to
the system. The sorting groups are defined by an internal
competition among cluster processor circuits 10. Each currently
operating cluster processor circuit represents a different sorting
group corresponding to a different type of input pulse parameter
data signal received. If the calculated probability density
function estimation value signal of each currently operating
cluster processor is less than at least a first threshold value
signal, then a match does not exist between the current unassigned
input pulse parameter data signal and the assigned input pulse
parameter data signal stored in each currently operating cluster
processor circuit. Therefore, a previously non-operating cluster
processor circuit will be activated to establish a newly operating
cluster processor circuit for storing the current input pulse
parameter data signal.
The input pulse parameter data signal 18 introduced to the APNN
system can represent any collection of measured pulse data. In the
preferred embodiment, the input pulse parameter data signal is
represented by a series of pulse signal parameters X where
and
FF=fine frequency
PA=pulse amplitude
PW=pulse width
AOA=angle of arrival
For certain ESM systems, the AOA measurements are expressed by
relative phases between three antennas to a reference antenna which
itself corresponds to a vector of three components where:
Initially, when the system is activated, only a first cluster
processor circuit of the plurality of cluster processor circuits is
"currently operating." All other cluster processor circuits within
the APNN system are "non-operating." The APNN system is initialized
by receiving and providing the first input pulse parameter data
signal 18 into the currently operating first cluster processor
circuit. As the first input pulse parameter data signal is
introduced to the APNN system, it is provided to each processing
element 15 of the currently operating first cluster processor
circuit. A probability density function estimation value signal is
then generated in the currently operating first cluster processor
circuit by the combined effects of the processing elements, the
exponential function circuits, and the summation circuit. The
probability density function estimation value signal is outputted
by the summation circuit of the currently operating first cluster
processor circuit according to the first input pulse parameter data
signal.
The probability density function estimation value signal is
generated according to the formula: ##EQU1## where PR=probability
density function estimation value
X=the current input pulse parameter data
W=previous input pulse parameter which is currently stored in a
register of the pulse buffer
i=cluster processor number corresponding to the emitter group or
bin number
j=current input data pulse signal parameter being analyzed
Sigma=smoothing factor which represents the standard deviation of
the probability density function (a constant set by the system
operator)
M=total number of parameters contained in the input data pulse
(i.e., FF, PA, PW . . . )
EXP=the exponential function
The probability density function estimation value signal is
generated by the currently operating first cluster processor
circuit in the following manner. The current unassigned input pulse
parameter data signal to be sorted is received and provided to each
processing element 15 of the first cluster processor circuit. Each
processing element determines a value for the expression:
##EQU2##
The above expression correlates to subtracting each parameter of
the assigned input pulse parameter data signals stored in the
registers 14 of the pulse buffer memory circuit 19 from the current
unassigned input pulse parameter data signal. Since the current
unassigned input pulse parameter data signal is the first data
signal provided to the APNN system, there are no signals stored in
the serially connected registers of the pulse buffer memory
circuit. To account for the lack of stored data in the pulse buffer
memory circuit, logic zeros are transmitted from each serially
connected register to each processing element so that a value for
the above expression can be generated. The difference between the
input pulse parameter data signals stored in the registers (here it
is logic zero) and the current unassigned input pulse parameter
data signal is then squared and divided by Sigma.sup.2, where Sigma
has a constant value. The resulting value is provided to the
exponential function circuit 16. The exponential function circuit
performs an exponential function and generates a signal which is
provided to the summation circuit 17.
The summation circuit 17 adds all of the output signals of the
exponential function circuits 16. The output signal of the
summation circuit, which corresponds to the output signal of the
cluster processor circuit 10, is a measure of the probability or
likelihood that the current unassigned input pulse parameter data
signal matches the assigned input pulse parameter data signal
stored in the pulse buffer memory circuit of the respective cluster
processor circuit. For example, a probability density function
estimation value signal of 80% represents a high probability of
correctly sorting the current unassigned input pulse parameter data
signal if the current unassigned input pulse parameter data signal
is stored in the respective cluster processor circuit. However, a
probability density function estimation value signal of 10%
represents a decisive mismatch for the current unassigned input
pulse parameter data signal and the assigned input pulse parameter
data signal stored in the respective cluster processor circuit.
The decision logic circuit 11 contains at least a first threshold
value signal. The decision logic circuit receives and compares the
probability density function estimation value signal of the
currently operating first cluster processor circuit to at least the
first threshold value signal. The decision logic circuit comparison
determines whether the current unassigned input pulse parameter
data signal should be stored in the currently operating first
cluster processor circuit or whether a non-operating cluster
processor circuit should be activated to store the current
unassigned input pulse parameter data signal. A newly operating
cluster processor circuit represents a new sorting classification
of input pulse parameter data signals received. In the preferred
embodiment, two threshold value signals are utilized. They are the
70% and 10% threshold value signals. If it is assumed that the
probability density function estimation value signal for the
currently operating first cluster processor circuit is at least
equal to the 70% threshold value signal, then the current
unassigned input pulse parameter data signal will be stored in the
currently operating first cluster processor circuit. Therefore,
after comparing the probability density function estimation value
signal of the currently operating first cluster processor circuit
with the 70% and 10% threshold value signals, the decision logic
circuit will generate and provide a decision address signal to the
switching circuit 13 corresponding to the currently operating first
cluster processor circuit. The decision address signal directs the
switching circuit to transmit the current unassigned input pulse
parameter data signal to the first register of the pulse buffer
memory circuit 19 for temporary storage.
If the probability density function estimation value of the
currently operating first cluster processor circuit is at most
equal to the 10% threshold value signal, then a decision address
signal will be transmitted instructing the switching Circuit to
activate a previously non-operating cluster processor circuit. The
previously non-operating cluster processor circuit is now referred
to as a newly operating cluster processor circuit. The switching
circuit then transmits the current unassigned input pulse parameter
data signal to the first register of the pulse buffer memory
circuit of the newly operating cluster processor circuit for
temporary storage. The activation of the newly operating cluster
processor circuit corresponds to a new type of input pulse
parameter data signal received by the APNN system.
If the probability density function estimation value signal
generated by the currently operating first cluster processor
circuit is greater than the 10% and less than the 70% threshold
value signals, then a different decision address signal is
transmitted by the decision logic circuit 11 to the switching
circuit 13. This decision address signal instructs the switching
circuit to assign the current unassigned input pulse parameter data
signal to a storage register circuit 12 for temporary storage. The
input pulse parameter data signal stored in the storage register
circuit is saved so that the APNN system can analyze the stored
input pulse parameter data signal in greater detail at a later
time. The input pulse parameter data signal stored in the storage
register circuit is not used in subsequent calculations of
probability density function estimation value signals. If desired,
this unassigned input pulse parameter data signal stored in the
storage register circuit can undergo a second level of analysis
called deinterleaving. After the data has been deinterleaved, it
can be sorted and stored in any currently operating or newly
operating cluster processor circuit.
The probability density function estimation value signal graph for
the first cluster processor circuit containing the first input
pulse parameter data signal is shown in FIG. 3. The probability
density function estimation is shown by a two variable, two
dimensional graph of, for example, coarse AOA vs fine AOA, as shown
by reference numeral 19. Similar graphs can be made having
different coordinate axes corresponding to the types of parameters
which represent the input pulse parameter data signal. From FIG. 3,
it is possible to measure statistical properties and to determine
sorting performance in a quantitative manner.
As a second unassigned input pulse parameter data signal (Pulse No.
2) enters the system, initialization is not needed because the
system has been operating. Therefore, the current unassigned input
pulse parameter data signal (Pulse No. 2) is provided to each
processing element of the first cluster processor circuit. The
previous input pulse parameter data signal (Pulse No. 1), which is
stored in the first register of the pulse buffer memory circuit 19
of the currently operating first cluster processor circuit, is
provided to the first processing element along with Pulse No. 2.
All other registers of the first cluster processor circuit which do
not contain data signals provide a logic zero to corresponding
processing elements. Each processing element generates and provides
a signal to the corresponding exponential function circuit 16. The
summation circuit 17 transmits the output signal of the cluster
processor circuit to the decision logic circuit 11. The decision
logic circuit performs a series of comparisons to see whether the
probability density function estimation value signal of the first
currently operating cluster processor circuit corresponding to
Pulse No. 2 is at least equal to the 70% threshold value signal, at
most equal to the 10% threshold value signal, or greater than the
10% and less than the 70% threshold value signal. If it is assumed
that the current unassigned input pulse parameter data signal
(Pulse No. 2) has less than a 10% probability density function
estimation value as determined by the currently operating first
cluster processor circuit, then the decision logic circuit provides
a decision address signal to the switching circuit 13 to activate a
previously non-operating cluster processor circuit. This newly
operating cluster processor circuit corresponds to a new
classification of input pulse parameter data signals. The switching
circuit then transmits the current unassigned input pulse parameter
data signal (Pulse No. 2) to the first register of the pulse buffer
memory circuit of the newly operating second cluster processor
circuit. The current unassigned input pulse parameter data signal
(Pulse No. 2) is now referred to as an assigned input pulse
parameter data signal. The probability density function estimation
value signal graph showing the first and second currently operating
cluster processor circuits and the corresponding assigned input
pulse parameter data signals is shown by reference numerals 19 and
20 in FIG. 4.
As the next current unassigned input pulse parameter data signal
(Pulse No. 3) is received and introduced to the adaptive
probabilistic neural network, signal generation by the two
currently operating cluster processor circuits is performed
concurrently (i.e., in parallel). This is a clear benefit to using
the neural network system over previous pulse sorting systems that
operated by a rule-base method. The present APNN parallel
processing system allows for numerous computations in a shorter
time period.
Assuming that the two currently operating cluster processor
circuits compute a probability density function estimation value
signal using Pulse No. 3, both probability density function
estimation value signals are transmitted by the summation circuit
of each currently operating cluster processor circuit to the
decision logic circuit 11. Once again, the previously stated
10%-70% threshold value signal comparison occurs. However, if both
currently operating cluster processor circuits have a probability
density function estimation value signal of greater than 70%, then
the decision logic circuit will output a decision address signal
corresponding to the currently operating cluster processor circuit
having the larger probability density function estimation value
signal. Assuming that the probability density function estimation
value signal of the first currently operating cluster processor is
larger than the probability density function estimation value
signal of the second currently operating cluster processor, and
further assuming that both estimation value signals are greater
than the 70% threshold value signal, then the decision logic
circuit will provide a decision address signal to the switching
circuit corresponding to the currently operating first cluster
processor circuit. Therefore, the switching circuit 13 assigns the
current unassigned input pulse parameter data signal (Pulse No. 3)
to the first register of the pulse buffer memory circuit 19 of the
currently operating first cluster processor circuit. Accordingly,
the previously assigned input pulse parameter data signal (Pulse
No. 1) that was stored in the first register of the pulse buffer
memory circuit of the currently operating first cluster processor
circuit is shifted to the second register of the pulse buffer
memory circuit. For each new input pulse parameter data signal that
is introduced to the pulse buffer memory circuit of the currently
operating first cluster processor circuit, the assigned input pulse
parameter data signal is shifted to the next register until the
assigned input pulse parameter data signal reaches the end of the
pulse buffer memory circuit. When the assigned input pulse
parameter data signal reaches the end of the pulse buffer memory
circuit, it is discarded.
FIG. 5 shows the probability density function estimation for the
two currently operating cluster processors after the APNN system
has received three input pulse parameter data signals. Reference
numeral 21 represents the probability density function estimation
value generated by the currently operating first cluster processor
having two input pulse parameter data signals sorted therein.
The real time adaptive probabilistic neural network of the present
invention is advantageously designed so that sorting of pulses at
pulse arrival rates of over 10 million/sec can be achieved based on
current VLSI technology. This is possible because the APNN system
generates output signals using parallel processing. Previously used
sorting methods generated output signals sequentially. The faster
parallel processing enables the current APNN system to achieve a
real time response.
Since the sorting process is based on a joint probability of the
entire input pulse parameter data signal matching the input pulse
parameter data signals previously sorted and stored in each cluster
processor circuit, the system is capable of producing approximate
results from noisy and incomplete data. This yields increased
sorting accuracy as compared with previous sorting systems.
The adaptive probabilistic neural network of the present invention
is also advantageously designed to provide a measure of the amount
of error that will be tolerated by the system. This corresponds to
the threshold value signals which are set by the system operator
(i.e., 10% and 70% threshold value signals). The threshold value
signals can be selected as desired.
Even though the above description of the real time data sorting
adaptive probabilistic neural network specifically referred to
"pulsed signals", the neural network system may also be applied to
continuous wave (CW) signals which may then be sorted as previously
described.
In addition to the above description of radar pulse sorting, a
probabilistic neural network can also be specifically utilized for
assisting in the detection, identification and tracking of objects
proximate to a motor vehicle for collision warning and avoidance.
Collision avoidance systems typically emit modulated, continuous
radio waves at specific frequencies and measure the reflected
received signal. The reflected signal of the radio waves are
typically shifted in the frequency domain if the object is moving.
Therefore, the faster the monitored object is traveling, the more
the frequency of the reflected signal is shifted.
Referring now to FIG. 6 of the drawings, a preferred apparatus for
motion detection of objects in a region for collision avoidance
constructed in accordance with the present invention will now be
described. The apparatus may be placed at a variety of locations on
a motor vehicle such as the front, sides and rear of the vehicle to
provide collision avoidance protection. The apparatus for detecting
motion of objects 50 includes a signal transmitter 52, electrically
coupled to first and second signal generators 54,56. The signal
transmitter 52 preferably includes first and second input ports
which receive first and second detection signals provided by first
and second signal generators 54,56. In addition, the signal
transmitter includes at least one output port 62. The first and
second detection signals generated by the signal generators
preferably have substantially distinct signal frequencies. In a
preferred embodiment, the frequencies of the first and second
detection signals are separated by approximately 250 kHz. The
signal generators may preferably be Gunn oscillators or dielectric
resonant oscillators such as Part No. DE2011 manufactured by GEC
Plessy Semiconductors of the United Kingdom. The signal generators
may also include an amplifier to increase the power of the first
and second detection signals before transmission. Alternatively,
the signal transmitter 52 may include an amplifier or, amplifiers
may be coupled between the first signal generator 54 and the signal
transmitter 52 or coupled between the second signal generator 56
and the signal transmitter 52. The signal transmitter 52 preferably
provides the simultaneous transmission of the first and second
detection signals.
The apparatus for collision avoidance 50 preferably includes an
antenna 64 electrically coupled to the signal transmitter output
port 62 for providing a radio frequency (rf) signal which includes
the first and second detection signals generated by the first and
second signal generators 54,56. Preferably, the (rf) signal
provided by the antenna consists of distinct signal components
including the first detection signal and is provided to a spatial
region proximate to the antenna. The (rf) signal is specifically
chosen so that the signal will at least partially reflect off of
objects which encounter the signal after transmission by the
antenna. In a preferred from of the present invention, the antenna
is an etched phased array antenna such as Part No. DE2006
manufactured by GEC Plessy Semiconductors of the United Kingdom.
Alternatively, Part No. DE2001 manufactured by GEC Plessy
Semiconductors may be utilized wherein the signal transmitter and
signal receivers are integral with the etched phased array antenna
and oscillator circuits.
The collision avoidance apparatus 50 also preferably includes a
signal receiver 66 having an input port 68 and an output port 70
wherein the input port is electrically coupled to the antenna 64.
In a preferred embodiment and as shown in FIG. 6, the antenna is
coupled to both the signal transmitter output port 62 and the
signal receiver input port 68 so that the antenna both sends and
receives the first and second detection signals. After the antenna
transmits the first and second detection signals to the spatial
region, the signals preferably reflect off objects located in the
region and at least a portion of the signal is transmitted (i.e.,
reflected back) to the signal receiver. The antenna is designed to
receive the reflected signal beam (including the first and second
detection signals) which has been reflected by the object, and to
provide the received signal to the signal receiver.
In a preferred embodiment of the present invention, the reflected
first and second object parameter data signals, once received by
the signal receiver 66 are provided on the signal receiver output
port 70 to a switching device 72 which preferably separates the
first and second object parameter data signals. A suitable
switching device for separating the first and second object
parameter data signals is Part No. LF13331 manufactured by National
Semiconductor Corporation which is a JFET analog switch. The signal
receiver 70 may also include an amplifier (not shown) for
increasing the strength of the first and second object parameter
data signals provided by the signal receiver 66 to the switch 72.
Alternatively, a separate amplifier may be coupled between the
signal receiver and the switch. The switching device 72 preferably
includes an input port 74, for receiving first and second object
parameter data signals from the signal receiver, and at least first
and second output ports 76,78 for providing the first object
parameter data signal on the first output port 76 and the second
object parameter data signal on the second output port 78.
The first and second output ports of the switch are preferably
electrically coupled to a low pass filter 80 for removal of noise
and unwanted portions of the first and second object parameter data
signals. In a preferred embodiment of the invention wherein the
operating frequency of first and second object parameter data
signals operate at approximately 24 GHz, the low pass filter is
preferably approximately a 24 KHz filter. The filtered first and
second object parameter data signals are preferably provided to an
analog to digital (A/D) converter 82 which preferably has first and
second input ports 84,86 and first and second output ports 88,90.
Each of the first and second object parameter data signals are
preferably converted from analog signals to digital signals in the
A/D converter. A commercially available suitable part for the A/D
converter is Part No. CS5339 manufactured by Crystal Semiconductor
Corporation.
The A/D converter 82 provides a digital representation of the first
and second object parameter data signals which are respectively
provided on the first and second output ports 88,90 of the analog
to digital converter. In an alternative embodiment of the
invention, the collision avoidance apparatus 50 may also include a
second low pass filter (not shown) for enhancement of the digital
signal provided by the A/D converter. In this alternative form of
the present invention, the second low pass filter is a 7.3 kHz
filter for removing noise and unwanted signal components.
Referring now to FIGS. 6 and 7, the apparatus for motion detection
and tracking of objects in a region for collision avoidance may
further include a Fourier transform circuit 92 electrically coupled
to the analog to digital converter 82. Preferably, the Fourier
transform circuit 92 receives the digital first and second object
parameter data signals (shown in FIG. 7 as F.sub.1 and F.sub.2)
from the A/D converter at first and second Fourier transform input
ports 94,96. The Fourier transform circuit converts the digital
first and second object parameter data signals from a discrete time
domain signal to a discrete frequency domain signal. The Fourier
transform circuit 92 further includes first and second output ports
98,100 so as to provide first and second Fourier transform object
parameter data signals, each representing a spectral waveshape of
the digital first and second object parameter data signals. A
suitable commercially available Fourier transform circuit is Part
No. PDSP16510 manufactured by GEC Plessy Semiconductors of the
United Kingdom.
As shown in FIG. 7, the first and second Fourier transform object
parameter data signals provide a spectrum of the intensity of the
reflected first and second detection signals at specific intervals.
The output of the Fourier transform circuit is provided to a
velocity and range finder circuit 102 shown in FIG. 6 and more
specifically shown in FIG. 7. Preferably, the velocity and range
finder circuit includes a peak finder circuit 104 having its input
port 106 electrically coupled to the Fourier transform circuit
first output port 98 for receiving the first Fourier transform
object parameter data signal. The velocity and range finder also
includes a subtractor circuit 108, having at least first and second
input ports 110,112. The first input port 110 of the subtractor is
preferably electrically coupled to the peak finder output port 114,
and the second input port 112 of the subtractor is preferably
electrically coupled to the second output port 100 of the Fourier
transform circuit. A commercially available suitable peak finder is
Part No. 74F524 manufactured by Signetics, a subsidiary of the
Philips Corporation and a suitable subtractor is Part No. 74F283
also manufactured by Signetics.
Based upon the Fourier transform first object parameter data signal
received from the Fourier transform circuit 92, the peak finder 104
provides an output signal indicative of the velocity of the
monitored object. In contrast, the subtractor 108 receives both the
Fourier transform first and second object parameter data signal and
provides a subtractor output signal indicative of the difference
between the first and second object parameter data signals which
corresponds to the range (distance) of the monitored object from
the apparatus.
The output ports 116,118 of the velocity and range finder 102 (the
peak finder and subtractor) are preferably electrically coupled to
a probabilistic neural network (PNN) processor 120 wherein each
pulse buffer memory circuit of the PNN includes at least as many
registers as the number objects being monitored by the motion
detection and tracking apparatus for collision avoidance. The
probabilistic neural network processor is preferably configured and
operates as described with respect to FIGS. 1-5. Specifically, the
probabilistic neural network receives the velocity and range finder
output signal as inputs and, based upon previous velocity and range
finder output signals, classifies a current velocity and range
finder output signal utilizing a probability density function.
FIG. 8 illustrates a first probability density function contour map
(range of object vs. relative velocity) for a first data sample 122
processed by the apparatus. The concentric rings indicate various
probabilities of matching wherein the inner most circle represents
the highest probability of matching and the outermost circle
represents the lowest probability of matching. FIG. 9 illustrates a
second probability density function map showing the first sample
122 and a second sample 124. Since the second sample is not within
the probability density function regions set around the first
sample, probability density function regions are set around the
second sample. For each successive sample point, either a new
probability density function region is included or, if the current
sample point falls within one of the currently existing probability
density function contours, the current probability density function
contours are recomputed to show a new likelihood of a future sample
point matter such as shown in FIG. 10 wherein a third sample point
126 and the first sample point are combined to indicate the
likelihood of the next position of the object being monitored.
The operation of the apparatus for motion detection and tracking of
objects in a region for collision avoidance will now be described.
First and second detection signals are preferably generated in the
first and second signal generators 54,56 and provided to the signal
transmitter 52 which may amplify and process the first and second
detection signals before providing the signals to antenna 64 for
transmission to a spatial region. The signal transmitter 52
preferably combines the first and second detection signals by
modulation or multiplexing. Typically, the detection signal
transmitted by the antenna has an azimuth field of view of
approximately 4.degree. and an elevation field of view of
approximately 5.degree..
After transmission, the first and second detection signals, which
as previously stated have substantially different frequencies, at
least partially reflected off objects in the field of view. As a
result, at least a portion of the signal reflects back to the
antenna 64 which provides the received detection signals to a
signal receiver 66. The signal receiver processes the received
detection signals (i.e., including amplification) and provides the
first and second detection signal to a switch 72. The switch 72 is
preferably designed to separate the first detection signal from the
second detection signal by switching.
Two switch output signals (i.e., first and second object parameter
data signals), corresponding to the first and second detection
signals, are provided by the switch to a low pass filter for
removing noise and unwanted signal components. The low pass filter
output signals are provided to an analog to digital converter which
converts the continuous radio frequency signals to digital signals.
The digital first and second A/D converter output signals are
provided to a Fourier transform circuit which transforms a time
domain signal (the received first and second detection signals
corresponding to the first and second object parameter data
signals) to a frequency domain signal. From the frequency domain
signals, the velocity and range of the object being monitored are
respectively determined by identifying the peak amplitude of one of
the first and second object parameter data signals and by taking
the difference between the first and second object parameter data
signals. The output signal of the velocity and range finder is
thereafter provided to the probabilistic neural network for
determining whether a current data sample is properly associated
with a prior data sample shown on a current probability density
function contour map or whether the new data sample corresponds to
a new object in the field of view of the antenna. By utilizing the
probabilistic neural network, it is possible to associate current
data samples corresponding to the present location of objects in
the antenna field of view based upon previous locations of objects
in the field of view. Therefore, it is possible to determine if an
object is moving farther away and therefore the threat of collision
is decreasing, or whether an object is moving closer and therefore
a threat of collision is increasing.
Although an illustrative embodiment of the present invention has
been described herein with reference to the accompanying drawings,
it is to be understood that the invention is not limited to the
precise embodiment, and that various other changes and
modifications may be effected therein by one skilled in the art
without departing from the scope or spirit of the invention.
* * * * *